Analysis of 111/999 NHS calls and 111 online reports
Quentin Leclerc, Emily Nightingale, Samuel Abbott & Thibaut Jombart, for CMMID
Wednesday 27 May 2020
1 Data preparation
1.1 Outline
Load scripts: loads libraries and useful scripts used in the analyses; all
.Rfiles contained inscriptsat the root of the factory are automatically loadedLoad data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
1.2 Load packages
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)1.3 Load scripts
These scripts will load:
- all scripts stored as
.Rfiles inside/scripts/ - all scripts stored as
.Rfiles inside/src/
These scripts also contain routines to access the latest clean encrypted data (see next section).
1.4 Load clean data
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 122,693 x 9[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 female 0-18 e380000… nhs_bar… 35 rm13ae london
## [90m 2[39m 111 2020-03-18 female 0-18 e380000… nhs_bed… 27 mk454hr east_of_e…
## [90m 3[39m 111 2020-03-18 female 0-18 e380000… nhs_bla… 9 bb12fd north_west
## [90m 4[39m 111 2020-03-18 female 0-18 e380000… nhs_bro… 11 br33ql london
## [90m 5[39m 111 2020-03-18 female 0-18 e380000… nhs_can… 9 ws111jp midlands
## [90m 6[39m 111 2020-03-18 female 0-18 e380000… nhs_cit… 12 n15lz london
## [90m 7[39m 111 2020-03-18 female 0-18 e380000… nhs_enf… 7 en40dy london
## [90m 8[39m 111 2020-03-18 female 0-18 e380000… nhs_ham… 6 dl62uu north_eas…
## [90m 9[39m 111 2020-03-18 female 0-18 e380000… nhs_har… 24 ts232la north_eas…
## [90m10[39m 111 2020-03-18 female 0-18 e380000… nhs_kin… 6 kt11eu london
## [90m# … with 122,683 more rows[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 11
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 42
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 61
## 33 2020-04-02 East of England 64
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 92
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 91
## 43 2020-04-12 East of England 101
## 44 2020-04-13 East of England 77
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 63
## 50 2020-04-19 East of England 66
## 51 2020-04-20 East of England 66
## 52 2020-04-21 East of England 74
## 53 2020-04-22 East of England 66
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 64
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 43
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 35
## 67 2020-05-06 East of England 28
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 30
## 70 2020-05-09 East of England 26
## 71 2020-05-10 East of England 21
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 25
## 76 2020-05-15 East of England 18
## 77 2020-05-16 East of England 25
## 78 2020-05-17 East of England 15
## 79 2020-05-18 East of England 16
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 22
## 82 2020-05-21 East of England 18
## 83 2020-05-22 East of England 9
## 84 2020-05-23 East of England 6
## 85 2020-05-24 East of England 9
## 86 2020-05-25 East of England 6
## 87 2020-03-01 London 0
## 88 2020-03-02 London 0
## 89 2020-03-03 London 0
## 90 2020-03-04 London 0
## 91 2020-03-05 London 0
## 92 2020-03-06 London 1
## 93 2020-03-07 London 1
## 94 2020-03-08 London 0
## 95 2020-03-09 London 1
## 96 2020-03-10 London 0
## 97 2020-03-11 London 7
## 98 2020-03-12 London 6
## 99 2020-03-13 London 10
## 100 2020-03-14 London 14
## 101 2020-03-15 London 10
## 102 2020-03-16 London 17
## 103 2020-03-17 London 25
## 104 2020-03-18 London 31
## 105 2020-03-19 London 25
## 106 2020-03-20 London 45
## 107 2020-03-21 London 49
## 108 2020-03-22 London 54
## 109 2020-03-23 London 63
## 110 2020-03-24 London 86
## 111 2020-03-25 London 112
## 112 2020-03-26 London 130
## 113 2020-03-27 London 129
## 114 2020-03-28 London 122
## 115 2020-03-29 London 147
## 116 2020-03-30 London 148
## 117 2020-03-31 London 180
## 118 2020-04-01 London 201
## 119 2020-04-02 London 189
## 120 2020-04-03 London 196
## 121 2020-04-04 London 229
## 122 2020-04-05 London 194
## 123 2020-04-06 London 198
## 124 2020-04-07 London 219
## 125 2020-04-08 London 236
## 126 2020-04-09 London 202
## 127 2020-04-10 London 168
## 128 2020-04-11 London 175
## 129 2020-04-12 London 156
## 130 2020-04-13 London 165
## 131 2020-04-14 London 142
## 132 2020-04-15 London 142
## 133 2020-04-16 London 138
## 134 2020-04-17 London 99
## 135 2020-04-18 London 101
## 136 2020-04-19 London 102
## 137 2020-04-20 London 94
## 138 2020-04-21 London 93
## 139 2020-04-22 London 108
## 140 2020-04-23 London 77
## 141 2020-04-24 London 71
## 142 2020-04-25 London 57
## 143 2020-04-26 London 53
## 144 2020-04-27 London 51
## 145 2020-04-28 London 43
## 146 2020-04-29 London 43
## 147 2020-04-30 London 39
## 148 2020-05-01 London 41
## 149 2020-05-02 London 40
## 150 2020-05-03 London 35
## 151 2020-05-04 London 29
## 152 2020-05-05 London 25
## 153 2020-05-06 London 35
## 154 2020-05-07 London 35
## 155 2020-05-08 London 29
## 156 2020-05-09 London 22
## 157 2020-05-10 London 25
## 158 2020-05-11 London 16
## 159 2020-05-12 London 17
## 160 2020-05-13 London 16
## 161 2020-05-14 London 20
## 162 2020-05-15 London 18
## 163 2020-05-16 London 14
## 164 2020-05-17 London 15
## 165 2020-05-18 London 9
## 166 2020-05-19 London 13
## 167 2020-05-20 London 18
## 168 2020-05-21 London 11
## 169 2020-05-22 London 5
## 170 2020-05-23 London 5
## 171 2020-05-24 London 4
## 172 2020-05-25 London 1
## 173 2020-03-01 Midlands 0
## 174 2020-03-02 Midlands 0
## 175 2020-03-03 Midlands 1
## 176 2020-03-04 Midlands 0
## 177 2020-03-05 Midlands 0
## 178 2020-03-06 Midlands 0
## 179 2020-03-07 Midlands 0
## 180 2020-03-08 Midlands 3
## 181 2020-03-09 Midlands 1
## 182 2020-03-10 Midlands 0
## 183 2020-03-11 Midlands 2
## 184 2020-03-12 Midlands 6
## 185 2020-03-13 Midlands 5
## 186 2020-03-14 Midlands 4
## 187 2020-03-15 Midlands 5
## 188 2020-03-16 Midlands 11
## 189 2020-03-17 Midlands 8
## 190 2020-03-18 Midlands 13
## 191 2020-03-19 Midlands 8
## 192 2020-03-20 Midlands 28
## 193 2020-03-21 Midlands 13
## 194 2020-03-22 Midlands 31
## 195 2020-03-23 Midlands 33
## 196 2020-03-24 Midlands 41
## 197 2020-03-25 Midlands 48
## 198 2020-03-26 Midlands 64
## 199 2020-03-27 Midlands 72
## 200 2020-03-28 Midlands 89
## 201 2020-03-29 Midlands 92
## 202 2020-03-30 Midlands 90
## 203 2020-03-31 Midlands 123
## 204 2020-04-01 Midlands 140
## 205 2020-04-02 Midlands 142
## 206 2020-04-03 Midlands 124
## 207 2020-04-04 Midlands 150
## 208 2020-04-05 Midlands 164
## 209 2020-04-06 Midlands 140
## 210 2020-04-07 Midlands 123
## 211 2020-04-08 Midlands 185
## 212 2020-04-09 Midlands 138
## 213 2020-04-10 Midlands 127
## 214 2020-04-11 Midlands 142
## 215 2020-04-12 Midlands 138
## 216 2020-04-13 Midlands 120
## 217 2020-04-14 Midlands 116
## 218 2020-04-15 Midlands 147
## 219 2020-04-16 Midlands 101
## 220 2020-04-17 Midlands 118
## 221 2020-04-18 Midlands 115
## 222 2020-04-19 Midlands 91
## 223 2020-04-20 Midlands 107
## 224 2020-04-21 Midlands 86
## 225 2020-04-22 Midlands 77
## 226 2020-04-23 Midlands 102
## 227 2020-04-24 Midlands 77
## 228 2020-04-25 Midlands 72
## 229 2020-04-26 Midlands 81
## 230 2020-04-27 Midlands 74
## 231 2020-04-28 Midlands 68
## 232 2020-04-29 Midlands 53
## 233 2020-04-30 Midlands 54
## 234 2020-05-01 Midlands 64
## 235 2020-05-02 Midlands 51
## 236 2020-05-03 Midlands 52
## 237 2020-05-04 Midlands 61
## 238 2020-05-05 Midlands 58
## 239 2020-05-06 Midlands 56
## 240 2020-05-07 Midlands 48
## 241 2020-05-08 Midlands 34
## 242 2020-05-09 Midlands 37
## 243 2020-05-10 Midlands 41
## 244 2020-05-11 Midlands 32
## 245 2020-05-12 Midlands 45
## 246 2020-05-13 Midlands 38
## 247 2020-05-14 Midlands 32
## 248 2020-05-15 Midlands 38
## 249 2020-05-16 Midlands 34
## 250 2020-05-17 Midlands 30
## 251 2020-05-18 Midlands 33
## 252 2020-05-19 Midlands 31
## 253 2020-05-20 Midlands 33
## 254 2020-05-21 Midlands 28
## 255 2020-05-22 Midlands 16
## 256 2020-05-23 Midlands 21
## 257 2020-05-24 Midlands 11
## 258 2020-05-25 Midlands 3
## 259 2020-03-01 North East and Yorkshire 0
## 260 2020-03-02 North East and Yorkshire 0
## 261 2020-03-03 North East and Yorkshire 0
## 262 2020-03-04 North East and Yorkshire 0
## 263 2020-03-05 North East and Yorkshire 0
## 264 2020-03-06 North East and Yorkshire 0
## 265 2020-03-07 North East and Yorkshire 0
## 266 2020-03-08 North East and Yorkshire 0
## 267 2020-03-09 North East and Yorkshire 0
## 268 2020-03-10 North East and Yorkshire 0
## 269 2020-03-11 North East and Yorkshire 0
## 270 2020-03-12 North East and Yorkshire 0
## 271 2020-03-13 North East and Yorkshire 0
## 272 2020-03-14 North East and Yorkshire 0
## 273 2020-03-15 North East and Yorkshire 2
## 274 2020-03-16 North East and Yorkshire 3
## 275 2020-03-17 North East and Yorkshire 1
## 276 2020-03-18 North East and Yorkshire 2
## 277 2020-03-19 North East and Yorkshire 6
## 278 2020-03-20 North East and Yorkshire 5
## 279 2020-03-21 North East and Yorkshire 6
## 280 2020-03-22 North East and Yorkshire 7
## 281 2020-03-23 North East and Yorkshire 9
## 282 2020-03-24 North East and Yorkshire 7
## 283 2020-03-25 North East and Yorkshire 18
## 284 2020-03-26 North East and Yorkshire 21
## 285 2020-03-27 North East and Yorkshire 28
## 286 2020-03-28 North East and Yorkshire 35
## 287 2020-03-29 North East and Yorkshire 38
## 288 2020-03-30 North East and Yorkshire 64
## 289 2020-03-31 North East and Yorkshire 60
## 290 2020-04-01 North East and Yorkshire 67
## 291 2020-04-02 North East and Yorkshire 74
## 292 2020-04-03 North East and Yorkshire 99
## 293 2020-04-04 North East and Yorkshire 104
## 294 2020-04-05 North East and Yorkshire 92
## 295 2020-04-06 North East and Yorkshire 95
## 296 2020-04-07 North East and Yorkshire 102
## 297 2020-04-08 North East and Yorkshire 107
## 298 2020-04-09 North East and Yorkshire 111
## 299 2020-04-10 North East and Yorkshire 117
## 300 2020-04-11 North East and Yorkshire 98
## 301 2020-04-12 North East and Yorkshire 84
## 302 2020-04-13 North East and Yorkshire 94
## 303 2020-04-14 North East and Yorkshire 107
## 304 2020-04-15 North East and Yorkshire 95
## 305 2020-04-16 North East and Yorkshire 103
## 306 2020-04-17 North East and Yorkshire 86
## 307 2020-04-18 North East and Yorkshire 95
## 308 2020-04-19 North East and Yorkshire 87
## 309 2020-04-20 North East and Yorkshire 100
## 310 2020-04-21 North East and Yorkshire 76
## 311 2020-04-22 North East and Yorkshire 83
## 312 2020-04-23 North East and Yorkshire 62
## 313 2020-04-24 North East and Yorkshire 72
## 314 2020-04-25 North East and Yorkshire 68
## 315 2020-04-26 North East and Yorkshire 63
## 316 2020-04-27 North East and Yorkshire 65
## 317 2020-04-28 North East and Yorkshire 57
## 318 2020-04-29 North East and Yorkshire 69
## 319 2020-04-30 North East and Yorkshire 56
## 320 2020-05-01 North East and Yorkshire 64
## 321 2020-05-02 North East and Yorkshire 48
## 322 2020-05-03 North East and Yorkshire 39
## 323 2020-05-04 North East and Yorkshire 48
## 324 2020-05-05 North East and Yorkshire 40
## 325 2020-05-06 North East and Yorkshire 50
## 326 2020-05-07 North East and Yorkshire 41
## 327 2020-05-08 North East and Yorkshire 38
## 328 2020-05-09 North East and Yorkshire 43
## 329 2020-05-10 North East and Yorkshire 39
## 330 2020-05-11 North East and Yorkshire 28
## 331 2020-05-12 North East and Yorkshire 25
## 332 2020-05-13 North East and Yorkshire 27
## 333 2020-05-14 North East and Yorkshire 28
## 334 2020-05-15 North East and Yorkshire 30
## 335 2020-05-16 North East and Yorkshire 35
## 336 2020-05-17 North East and Yorkshire 26
## 337 2020-05-18 North East and Yorkshire 26
## 338 2020-05-19 North East and Yorkshire 27
## 339 2020-05-20 North East and Yorkshire 20
## 340 2020-05-21 North East and Yorkshire 29
## 341 2020-05-22 North East and Yorkshire 19
## 342 2020-05-23 North East and Yorkshire 14
## 343 2020-05-24 North East and Yorkshire 16
## 344 2020-05-25 North East and Yorkshire 9
## 345 2020-03-01 North West 0
## 346 2020-03-02 North West 0
## 347 2020-03-03 North West 0
## 348 2020-03-04 North West 0
## 349 2020-03-05 North West 1
## 350 2020-03-06 North West 0
## 351 2020-03-07 North West 0
## 352 2020-03-08 North West 1
## 353 2020-03-09 North West 0
## 354 2020-03-10 North West 0
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## 356 2020-03-12 North West 2
## 357 2020-03-13 North West 3
## 358 2020-03-14 North West 1
## 359 2020-03-15 North West 4
## 360 2020-03-16 North West 2
## 361 2020-03-17 North West 4
## 362 2020-03-18 North West 6
## 363 2020-03-19 North West 6
## 364 2020-03-20 North West 10
## 365 2020-03-21 North West 11
## 366 2020-03-22 North West 13
## 367 2020-03-23 North West 15
## 368 2020-03-24 North West 21
## 369 2020-03-25 North West 20
## 370 2020-03-26 North West 29
## 371 2020-03-27 North West 35
## 372 2020-03-28 North West 27
## 373 2020-03-29 North West 46
## 374 2020-03-30 North West 66
## 375 2020-03-31 North West 52
## 376 2020-04-01 North West 85
## 377 2020-04-02 North West 95
## 378 2020-04-03 North West 94
## 379 2020-04-04 North West 98
## 380 2020-04-05 North West 102
## 381 2020-04-06 North West 100
## 382 2020-04-07 North West 133
## 383 2020-04-08 North West 125
## 384 2020-04-09 North West 119
## 385 2020-04-10 North West 116
## 386 2020-04-11 North West 135
## 387 2020-04-12 North West 126
## 388 2020-04-13 North West 125
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## 390 2020-04-15 North West 114
## 391 2020-04-16 North West 133
## 392 2020-04-17 North West 96
## 393 2020-04-18 North West 112
## 394 2020-04-19 North West 70
## 395 2020-04-20 North West 80
## 396 2020-04-21 North West 75
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## 398 2020-04-23 North West 85
## 399 2020-04-24 North West 65
## 400 2020-04-25 North West 65
## 401 2020-04-26 North West 54
## 402 2020-04-27 North West 54
## 403 2020-04-28 North West 56
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## 405 2020-04-30 North West 57
## 406 2020-05-01 North West 43
## 407 2020-05-02 North West 55
## 408 2020-05-03 North West 54
## 409 2020-05-04 North West 44
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## 411 2020-05-06 North West 41
## 412 2020-05-07 North West 44
## 413 2020-05-08 North West 41
## 414 2020-05-09 North West 28
## 415 2020-05-10 North West 38
## 416 2020-05-11 North West 32
## 417 2020-05-12 North West 35
## 418 2020-05-13 North West 24
## 419 2020-05-14 North West 26
## 420 2020-05-15 North West 33
## 421 2020-05-16 North West 30
## 422 2020-05-17 North West 23
## 423 2020-05-18 North West 26
## 424 2020-05-19 North West 31
## 425 2020-05-20 North West 23
## 426 2020-05-21 North West 20
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## 429 2020-05-24 North West 12
## 430 2020-05-25 North West 2
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## 471 2020-04-10 South East 88
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## 475 2020-04-14 South East 64
## 476 2020-04-15 South East 72
## 477 2020-04-16 South East 56
## 478 2020-04-17 South East 86
## 479 2020-04-18 South East 57
## 480 2020-04-19 South East 69
## 481 2020-04-20 South East 85
## 482 2020-04-21 South East 49
## 483 2020-04-22 South East 54
## 484 2020-04-23 South East 57
## 485 2020-04-24 South East 64
## 486 2020-04-25 South East 50
## 487 2020-04-26 South East 51
## 488 2020-04-27 South East 40
## 489 2020-04-28 South East 40
## 490 2020-04-29 South East 46
## 491 2020-04-30 South East 28
## 492 2020-05-01 South East 37
## 493 2020-05-02 South East 35
## 494 2020-05-03 South East 17
## 495 2020-05-04 South East 35
## 496 2020-05-05 South East 29
## 497 2020-05-06 South East 22
## 498 2020-05-07 South East 25
## 499 2020-05-08 South East 25
## 500 2020-05-09 South East 28
## 501 2020-05-10 South East 19
## 502 2020-05-11 South East 23
## 503 2020-05-12 South East 26
## 504 2020-05-13 South East 17
## 505 2020-05-14 South East 31
## 506 2020-05-15 South East 23
## 507 2020-05-16 South East 18
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## 539 2020-03-23 South West 9
## 540 2020-03-24 South West 7
## 541 2020-03-25 South West 9
## 542 2020-03-26 South West 11
## 543 2020-03-27 South West 13
## 544 2020-03-28 South West 21
## 545 2020-03-29 South West 18
## 546 2020-03-30 South West 23
## 547 2020-03-31 South West 23
## 548 2020-04-01 South West 22
## 549 2020-04-02 South West 23
## 550 2020-04-03 South West 30
## 551 2020-04-04 South West 42
## 552 2020-04-05 South West 32
## 553 2020-04-06 South West 34
## 554 2020-04-07 South West 39
## 555 2020-04-08 South West 47
## 556 2020-04-09 South West 24
## 557 2020-04-10 South West 46
## 558 2020-04-11 South West 43
## 559 2020-04-12 South West 23
## 560 2020-04-13 South West 26
## 561 2020-04-14 South West 24
## 562 2020-04-15 South West 31
## 563 2020-04-16 South West 29
## 564 2020-04-17 South West 33
## 565 2020-04-18 South West 25
## 566 2020-04-19 South West 31
## 567 2020-04-20 South West 26
## 568 2020-04-21 South West 26
## 569 2020-04-22 South West 22
## 570 2020-04-23 South West 17
## 571 2020-04-24 South West 19
## 572 2020-04-25 South West 15
## 573 2020-04-26 South West 27
## 574 2020-04-27 South West 13
## 575 2020-04-28 South West 17
## 576 2020-04-29 South West 14
## 577 2020-04-30 South West 26
## 578 2020-05-01 South West 6
## 579 2020-05-02 South West 6
## 580 2020-05-03 South West 10
## 581 2020-05-04 South West 16
## 582 2020-05-05 South West 14
## 583 2020-05-06 South West 18
## 584 2020-05-07 South West 16
## 585 2020-05-08 South West 5
## 586 2020-05-09 South West 10
## 587 2020-05-10 South West 5
## 588 2020-05-11 South West 7
## 589 2020-05-12 South West 7
## 590 2020-05-13 South West 7
## 591 2020-05-14 South West 6
## 592 2020-05-15 South West 3
## 593 2020-05-16 South West 4
## 594 2020-05-17 South West 6
## 595 2020-05-18 South West 4
## 596 2020-05-19 South West 6
## 597 2020-05-20 South West 1
## 598 2020-05-21 South West 8
## 599 2020-05-22 South West 5
## 600 2020-05-23 South West 5
## 601 2020-05-24 South West 2
## 602 2020-05-25 South West 21.5 Completion date
We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Tuesday 26 May 2020.
1.6 Add variables
We add the following variable:
day: an integer representing the number of days from the earliest data reported, used for modelling purposes; the first day is 0
1.7 Auxiliary functions
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
2 Temporal trends in NHS Pathways reports
2.1 Outline
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
2.2 Reports by region over time
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 8,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 6,
lab_pos_y = 30000,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_report
2.3 Reports by region by age over time
We plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
2.4 Moving window model
We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))
2.5 Moving window model - stratified by age
We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") 
3 Comparison with deaths time series
3.1 Outline
We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
3.2 Lagged correlation
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lag
This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -5.3841 -2.2211 0.0146 2.1274 6.5875
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.630e+00 5.541e-02 101.61 < 2e-16 ***
## note_lag 7.678e-06 5.297e-07 14.49 4.35e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 9.193402)
##
## Null deviance: 2220.40 on 31 degrees of freedom
## Residual deviance: 276.95 on 30 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 278.567116 1.000008
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 249.649377 310.214490
## note_lag 1.000007 1.000009
Rsq(lag_mod)
## [1] 0.87527
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fit
4 Supplementary figures
4.1 Serial interval distribution
This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") 
4.2 Sensitivity analysis - 7 or 21 days moving window
We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom") 
4.3 Correlation between NHS Pathways reports and deaths by NHS region
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_reg
5 Export data
We save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))6 System information
6.1 Outline
The following information documents the system on which the document was compiled.
6.2 System
This provides information on the operating system.
6.3 R environment
This provides information on the version of R used:
6.4 R packages
This provides information on the packages used:
sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.1 ggpubr_0.3.0 lubridate_1.7.8
## [4] chngpt_2020.5-21 cyphr_1.1.0 DT_0.13
## [7] kableExtra_1.1.0 janitor_2.0.1 remotes_2.1.1
## [10] projections_0.4.1 earlyR_0.0.1 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.1 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.5 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.0 stringr_1.4.0
## [22] dplyr_0.8.5 purrr_0.3.4 readr_1.3.1
## [25] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.0
## [28] tidyverse_1.3.0 here_0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 selectr_0.4-2 ggsignif_0.6.0 ellipsis_0.3.1
## [5] rprojroot_1.3-2 snakecase_0.11.0 fs_1.4.1 rstudioapi_0.11
## [9] farver_2.0.3 fansi_0.4.1 splines_3.6.3 knitr_1.28
## [13] jsonlite_1.6.1 broom_0.5.6 dbplyr_1.4.4 compiler_3.6.3
## [17] httr_1.4.1 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18
## [21] cli_2.0.2 htmltools_0.4.0 prettyunits_1.1.1 tools_3.6.3
## [25] gtable_0.3.0 glue_1.4.1 Rcpp_1.0.4.6 carData_3.0-4
## [29] cellranger_1.1.0 vctrs_0.3.0 nlme_3.1-144 matchmaker_0.1.1
## [33] crosstalk_1.1.0.1 xfun_0.14 ps_1.3.3 openxlsx_4.1.5
## [37] lifecycle_0.2.0 rstatix_0.5.0 MASS_7.3-51.5 scales_1.1.1
## [41] hms_0.5.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [45] gridExtra_2.3 stringi_1.4.6 kyotil_2019.11-22 boot_1.3-24
## [49] pkgbuild_1.0.8 zip_2.0.4 rlang_0.4.6 pkgconfig_2.0.3
## [53] evaluate_0.14 lattice_0.20-38 labeling_0.3 htmlwidgets_1.5.1
## [57] cowplot_1.0.0 processx_3.4.2 tidyselect_1.1.0 plyr_1.8.6
## [61] magrittr_1.5 R6_2.4.1 generics_0.0.2 DBI_1.1.0
## [65] pillar_1.4.4 haven_2.3.0 foreign_0.8-75 withr_2.2.0
## [69] mgcv_1.8-31 survival_3.1-8 abind_1.4-5 modelr_0.1.8
## [73] crayon_1.3.4 car_3.0-8 utf8_1.1.4 rmarkdown_2.1
## [77] viridis_0.5.1 grid_3.6.3 readxl_1.3.1 data.table_1.12.8
## [81] blob_1.2.1 callr_3.4.3 reprex_0.3.0 digest_0.6.25
## [85] webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0